Transfer Learning Based Skin Cancer Detection Using Convolutional Neural Networks

Skin cancer, a global health concern, requires early and accurate detection methods to improve patient outcomes. Despite significant advancements in deep learning, challenges like dataset imbalances and the trade-off between model accuracy and computational efficiency persist. This study introduces a comprehensive analysis of various Convolutional Neural Network (CNN) architectures for skin cancer detection using the HAM10000 dataset, comprising 10,015 dermatoscopic images of seven pigmented lesions. This research addresses class imbalances and enhances model robustness by implementing a data augmentation strategy combined with standard preprocessing techniques, such as image resizing and normalization. Six state-of-the-art CNN models—VGG16, VGG19, ResNet50, MobileNet, MobileNetV2, and MobileNetV3—are systematically evaluated to determine their effectiveness. The findings reveal that ResNet50 achieves the highest accuracy and F1-score, making it reliable for precise diagnosis. At the same time, MobileNetV3 excels in computational efficiency, suggesting its suitability for resource-constrained environments or real-time applications. This study provides critical insights into the trade-offs between accuracy and efficiency in CNN-based skin cancer detection, offering a practical framework for selecting the appropriate model based on specific application needs.